In the above image, It is the part of the resNet Architecture, here they have used dotted line to increase the dimension, but my question is How they are increasing the dimension?? or this dotted line is just a convolution layer to increase the dimension?
Solved – How resNet increasing the dimension
conv-neural-networkdeep learningmachine learningresidual-networks
Best Answer
That essentially means either linear skip connection, or padding $\mathbf{x}$ (input to the residual block) to appropriate shape.
Note that in equation $(2)$ of the ResNet paper:
$$\textbf{y} = \mathcal{F}(\textbf{x}, W_i) + W_s\textbf{x}$$
You can have $W_s$ mapping $\textbf{x}$ to the desired space.
Here is the excerpt on this from the paper: